
import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			def __init__(							self			:							str    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							Union[str, Any]   ):
							UpperCamelCase_:				Tuple						=    params

							UpperCamelCase_:				List[Any]						=    np.array(snake_case_   )
							UpperCamelCase_:				str						=    np.array([len(snake_case_   ) for t in data]   )

							self.check()
							self.remove_long_sequences()
							self.remove_empty_sequences()
							self.remove_unknown_sequences()
							self.check()
							self.print_statistics()

			def __getitem__(							self			:							Any    ,						snake_case_			:							Tuple   ):
							return (self.token_ids[index], self.lengths[index])

			def __len__(							self			:							Optional[Any]   ):
							return len(self.lengths   )

			def    lowerCAmelCase__						(							self			:							List[str]   ):
							assert len(self.token_ids   ) == len(self.lengths   )
							assert all(self.lengths[i] == len(self.token_ids[i]   ) for i in range(len(self.lengths   )   )   )

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				List[str]						=    self.params.max_model_input_size
							UpperCamelCase_:				Any						=    self.lengths > max_len
							logger.info(f'''Splitting {sum(snake_case_   )} too long sequences.'''   )

							def divide_chunks(snake_case_			:							Optional[Any]    ,						snake_case_			:							str   ):
											return [l[i : i + n] for i in range(0    ,						len(snake_case_   )    ,						snake_case_   )]

							UpperCamelCase_:				str						=    []
							UpperCamelCase_:				Dict						=    []
							if self.params.mlm:
											UpperCamelCase_, UpperCamelCase_:				Any						=    self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
							else:
											UpperCamelCase_, UpperCamelCase_:				Optional[Any]						=    self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]

							for seq_, len_ in zip(self.token_ids    ,						self.lengths   ):
											assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
											if len_ <= max_len:
															new_tok_ids.append(seq_   )
															new_lengths.append(len_   )
											else:
															UpperCamelCase_:				str						=    []
															for sub_s in divide_chunks(seq_    ,						max_len - 2   ):
																			if sub_s[0] != cls_id:
																							UpperCamelCase_:				Tuple						=    np.insert(snake_case_    ,						0    ,						snake_case_   )
																			if sub_s[-1] != sep_id:
																							UpperCamelCase_:				str						=    np.insert(snake_case_    ,						len(snake_case_   )    ,						snake_case_   )
																			assert len(snake_case_   ) <= max_len
																			assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																			sub_seqs.append(snake_case_   )

															new_tok_ids.extend(snake_case_   )
															new_lengths.extend([len(snake_case_   ) for l in sub_seqs]   )

							UpperCamelCase_:				List[str]						=    np.array(snake_case_   )
							UpperCamelCase_:				str						=    np.array(snake_case_   )

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Optional[int]						=    len(self   )
							UpperCamelCase_:				Dict						=    self.lengths > 11
							UpperCamelCase_:				Dict						=    self.token_ids[indices]
							UpperCamelCase_:				Union[str, Any]						=    self.lengths[indices]
							UpperCamelCase_:				List[Any]						=    len(self   )
							logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''   )

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							if "unk_token" not in self.params.special_tok_ids:
											return
							else:
											UpperCamelCase_:				str						=    self.params.special_tok_ids["""unk_token"""]
							UpperCamelCase_:				List[Any]						=    len(self   )
							UpperCamelCase_:				Union[str, Any]						=    np.array([np.count_nonzero(a == unk_token_id   ) for a in self.token_ids]   )
							UpperCamelCase_:				Tuple						=    (unk_occs / self.lengths) < 0.5
							UpperCamelCase_:				List[Any]						=    self.token_ids[indices]
							UpperCamelCase_:				List[str]						=    self.lengths[indices]
							UpperCamelCase_:				List[str]						=    len(self   )
							logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''   )

			def    lowerCAmelCase__						(							self			:							Dict   ):
							if not self.params.is_master:
											return
							logger.info(f'''{len(self   )} sequences'''   )
							# data_len = sum(self.lengths)
							# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
							# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

							# unk_idx = self.params.special_tok_ids['unk_token']
							# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
							# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')




			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							int   ):
							UpperCamelCase_:				Optional[Any]						=    [t[0] for t in batch]
							UpperCamelCase_:				Any						=    [t[1] for t in batch]
							assert len(snake_case_   ) == len(snake_case_   )

							# Max for paddings
							UpperCamelCase_:				List[str]						=    max(snake_case_   )

							# Pad token ids
							if self.params.mlm:
											UpperCamelCase_:				Any						=    self.params.special_tok_ids["""pad_token"""]
							else:
											UpperCamelCase_:				int						=    self.params.special_tok_ids["""unk_token"""]
							UpperCamelCase_:				Dict						=    [list(t.astype(snake_case_   )   ) + [pad_idx] * (max_seq_len_ - len(snake_case_   )) for t in token_ids]
							assert len(tk_   ) == len(snake_case_   )
							assert all(len(snake_case_   ) == max_seq_len_ for t in tk_   )

							UpperCamelCase_:				List[str]						=    torch.tensor(tk_   )  # (bs, max_seq_len_)
							UpperCamelCase_:				int						=    torch.tensor(snake_case_   )  # (bs)
							return tk_t, lg_t








from ..utils import is_flax_available, is_torch_available


if is_torch_available():
							from .autoencoder_kl import AutoencoderKL
							from .controlnet import ControlNetModel
							from .dual_transformer_ad import DualTransformeraDModel
							from .modeling_utils import ModelMixin
							from .prior_transformer import PriorTransformer
							from .ta_film_transformer import TaFilmDecoder
							from .transformer_ad import TransformeraDModel
							from .unet_ad import UNetaDModel
							from .unet_ad import UNetaDModel
							from .unet_ad_condition import UNetaDConditionModel
							from .unet_ad_condition import UNetaDConditionModel
							from .vq_model import VQModel

if is_flax_available():
							from .controlnet_flax import FlaxControlNetModel
							from .unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .vae_flax import FlaxAutoencoderKL






